القائمة
Sub-skill of polars: 6. Joins and Concatenation.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
استنادا إلى تصنيف SOC المهني
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/vamseeachanta/workspace-hub --skill polars-6-joins-and-concatenationيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
| name | polars-6-joins-and-concatenation |
| description | Sub-skill of polars: 6. Joins and Concatenation. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Join Operations:
import polars as pl
# Sample data
orders = pl.DataFrame({
"order_id": [1, 2, 3, 4, 5],
"customer_id": [101, 102, 101, 103, 104],
"amount": [250.0, 150.0, 300.0, 200.0, 175.0]
})
customers = pl.DataFrame({
"customer_id": [101, 102, 103, 105],
"name": ["Alice", "Bob", "Charlie", "Diana"],
"region": ["East", "West", "East", "North"]
})
# Inner join (default)
result = orders.join(customers, on="customer_id", how="inner")
# Left join
result = orders.join(customers, on="customer_id", how="left")
# Right join
result = orders.join(customers, on="customer_id", how="right")
# Outer/full join
result = orders.join(customers, on="customer_id", how="full")
# Cross join (cartesian product)
result = orders.join(customers, how="cross")
# Semi join (filter left by right)
result = orders.join(customers, on="customer_id", how="semi")
# Anti join (filter left NOT in right)
result = orders.join(customers, on="customer_id", how="anti")
# Join on multiple columns
df1 = pl.DataFrame({"a": [1, 2], "b": ["x", "y"], "val1": [10, 20]})
df2 = pl.DataFrame({"a": [1, 2], "b": ["x", "z"], "val2": [100, 200]})
result = df1.join(df2, on=["a", "b"], how="inner")
# Join with different column names
result = orders.join(
customers.rename({"customer_id": "cust_id"}),
left_on="customer_id",
right_on="cust_id"
)
# Join with suffix for duplicate columns
df1 = pl.DataFrame({"id": [1, 2], "value": [10, 20]})
df2 = pl.DataFrame({"id": [1, 2], "value": [100, 200]})
result = df1.join(df2, on="id", suffix="_right")
Concatenation:
# Vertical concatenation (stack rows)
df1 = pl.DataFrame({"a": [1, 2], "b": [3, 4]})
df2 = pl.DataFrame({"a": [5, 6], "b": [7, 8]})
df3 = pl.DataFrame({"a": [9, 10], "b": [11, 12]})
combined = pl.concat([df1, df2, df3])
# Horizontal concatenation (stack columns)
df1 = pl.DataFrame({"a": [1, 2, 3]})
df2 = pl.DataFrame({"b": [4, 5, 6]})
combined = pl.concat([df1, df2], how="horizontal")
# Diagonal concatenation (union with different schemas)
df1 = pl.DataFrame({"a": [1, 2], "b": [3, 4]})
df2 = pl.DataFrame({"b": [5, 6], "c": [7, 8]})
combined = pl.concat([df1, df2], how="diagonal")
# Align schemas before concat
df1 = pl.DataFrame({"a": [1, 2], "b": [3, 4]})
df2 = pl.DataFrame({"a": [5, ], : [, ]})
combined = pl.concat([df1, df2], how=)
Asof Joins (Time-based):
# For joining on nearest timestamp
trades = pl.DataFrame({
"time": pl.datetime_range(datetime(2025, 1, 1, 9, 0), datetime(2025, 1, 1, 9, 10), "1m", eager=True),
"price": [100.0, 101.0, 100.5, 102.0, 101.5, 103.0, 102.5, 104.0, 103.5, 105.0, 104.5]
})
quotes = pl.DataFrame({
"time": pl.datetime_range(datetime(2025, 1, 1, 9, 0), datetime(2025, 1, 1, 9, 10), "2m", eager=True),
"bid": [99.5, 100.5, 101.5, 102.5, 103.5, 104.5]
})
# Join each trade with the most recent quote
result = trades.join_asof(
quotes,
on="time",
strategy="backward" # Use most recent quote
)